Comparison between the microbiote states for the ages and sexes

Table of content

  1. Differentially expressed genes
  2. Stats about the DEG
  3. DEG with significant p-value and fold change
    1. Log2FC
    2. Z-score
  4. Co-expression (WGCNA))
    1. Z-score in modules
    2. Genes in modules
  5. Enrichment analysis
    1. GO analysis
    2. KEGG pathways

Generated from a Jupyter Notebook - Sources

Loads

Libraries and functions

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”
==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data

Extraction of the differentially expressed genes

Extract DEG between GF and SPF for the different ages and sex combinations

  • Threshold for adjusted p-value: 0.05
  • Threshold for adjusted significant fold change: 1.5

Table with the factors

InfoInterceptMale vs FemaleGF vs SPFMiddle-aged vs YoungOld vs YoungMale & Middle-agedMale & OldMale & GFGF & Middle-agedGF & Old
GF vs SPF (Female, Young) 0 0 1 0 0 0 0 0 0 0
GF vs SPF (Male, Young) 0 0 1 0 0 0 0 1 0 0
GF vs SPF (Female, Middle-aged)0 0 1 0 0 0 0 0 1 0
GF vs SPF (Male, Middle-aged) 0 0 1 0 0 0 0 1 1 0
GF vs SPF (Female, Old) 0 0 1 0 0 0 0 0 0 1
GF vs SPF (Male, Old) 0 0 1 0 0 0 0 1 0 1

Extract the log2FC of the DEG

Stats about the DEG

Using type as id variables
GF vs SPF (Female, Young)GF vs SPF (Male, Young)GF vs SPF (Female, Middle-aged)GF vs SPF (Male, Middle-aged)GF vs SPF (Female, Old)GF vs SPF (Male, Old)
All DEG (Wald padj < 0.05)256 127 877 426 19303329
All over-expressed genes (Wald padj < 0.05 & FC > 0)146 80 375 171 8661496
All under-expressed genes (Wald padj < 0.05 & FC < 0)110 47 502 255 10641833
DEG (Wald padj < 0.05 & abs(FC) >= 1.5)145 89 352 244 9991265
Over-expressed genes (Wald padj < 0.05 & FC >= 1.5) 92 58 136 84 311 324
Under-expressed genes (Wald padj < 0.05 & FC <= -1.5) 53 31 216 160 688 941

All DEG (Wald padj < 0.05)

DEG (Wald padj < 0.05 & abs(FC) > 1.5)

pdf: 2

DEG with significant p-value and fold change

Log2FC

ComparisonSexAge
GF vs SPF (Female, Young)GF VS SPF F Young
GF vs SPF (Male, Young)GF VS SPF M Young
GF vs SPF (Female, Middle-aged)GF VS SPF F Middle-Aged
GF vs SPF (Male, Middle-aged)GF VS SPF M Middle-Aged
GF vs SPF (Female, Old)GF VS SPF F Old
GF vs SPF (Male, Old)GF VS SPF M Old

Z-score

Column order: sex - age - microbiote

Column order: age - sex - microbiote

Subset of selected genes

pdf: 2

Co-expression (WGCNA)

Z-score in modules

Column order: sex - age - microbiote

Column order: age - sex - microbiote

Genes in modules

Enrichment analysis

Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”

GO analysis

Warning message in stack.default(getgo(l$sign_fc_deg$genes, "mm10", "geneSymbol")):
“non-vector elements will be ignored”

Biological process

Dot-plot with the 10 most significant p-values for the different comparison

Using category as id variables
Using category, type as id variables
Warning message:
“Column `variable` joining factors with different levels, coercing to character vector”

Cellular components

Dot-plot with the most over-represented CC GO (20 most significant p-values for the different comparison)

Using category as id variables
Using category, type as id variables
Warning message:
“Column `variable` joining factors with different levels, coercing to character vector”

Molecular functions

Dot-plot with the most over-represented MF GO (20 most significant p-values for the different comparison)

Using category as id variables
Using category, type as id variables
Warning message:
“Column `variable` joining factors with different levels, coercing to character vector”

GO networks

The edge colors in the tree represent the relationship between two nodes. - green: positively regulates

  • red: negatively regulates
  • black: regulates
  • blue: is a
  • light blue: part of

GO Trees at "../results/dge/microbiote-effect/microbiote-sex-age/go/"

[1] "GF vs SPF (Female, Young)"

Warning message:
“Column `values` has different attributes on LHS and RHS of join”Warning message:
“Column `values` has different attributes on LHS and RHS of join”
[1] "GF vs SPF (Female, Middle-aged)"

Warning message:
“Column `values` has different attributes on LHS and RHS of join”Warning message:
“Column `values` has different attributes on LHS and RHS of join”
[1] "GF vs SPF (Female, Old)"

Warning message:
“Column `values` has different attributes on LHS and RHS of join”Warning message:
“Column `values` has different attributes on LHS and RHS of join”
[1] "GF vs SPF (Male, Young)"

Warning message:
“Column `values` has different attributes on LHS and RHS of join”Warning message:
“Column `values` has different attributes on LHS and RHS of join”
[1] "GF vs SPF (Male, Middle-aged)"

Warning message:
“Column `values` has different attributes on LHS and RHS of join”Warning message:
“Column `values` has different attributes on LHS and RHS of join”
[1] "GF vs SPF (Male, Old)"

Warning message:
“Column `values` has different attributes on LHS and RHS of join”Warning message:
“Column `values` has different attributes on LHS and RHS of join”

KEGG pathways

Warning message in stack.default(getgo(l$sign_fc_deg$genes, "mm10", "geneSymbol", :
“non-vector elements will be ignored”
Error in `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", : replacement has 253 rows, data has 346
Traceback:

1. plot_kegg_pathways(deg$KEGG$over$category, deg$sign_fc_deg, paste("../results/dge/", 
 .     dir_path, "/kegg/over_repr_kegg/", sep = ""))
2. suppressMessages(pathview(gene.data = fc_deg, pathway.id = cat, 
 .     species = "Mus musculus", gene.idtype = "Symbol"))
3. withCallingHandlers(expr, message = function(c) invokeRestart("muffleMessage"))
4. pathview(gene.data = fc_deg, pathway.id = cat, species = "Mus musculus", 
 .     gene.idtype = "Symbol")
5. `$<-`(`*tmp*`, labels, value = c("", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
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 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", ""))
6. `$<-.data.frame`(`*tmp*`, labels, value = c("", "", "", "", "", 
 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
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 . "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
 . "", "", "", "", "", "", "", ""))
7. stop(sprintf(ngettext(N, "replacement has %d row, data has %d", 
 .     "replacement has %d rows, data has %d"), N, nrows), domain = NA)

Pathway graphs available at ../results/dge/type-effect/type_gender_age/over_repr_kegg/

Pathway graphs available at ../results/dge/type-effect/type_gender_age/under_repr_kegg/

Citations